Machine Learning · head to head
Comet ML vs Ray
Comet ML
Machine Learning
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; Ray windows support is beta and multi node Ray clusters are untested on Windows
- They diverge on capability: Comet ML covers Experiment tracking, Ray covers Distributed computing.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Comet ML and Ray actually diverge.
Identical on both: starting price (Free), pricing model (freemium), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- Keras
- Web support
Only in Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- Kubernetes
Both cover
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- LLM observability and monitoringnot Ray
- AI agent testing and debuggingnot Ray
- Experiment tracking for machine learningnot Ray
- Model registry and version managementnot Ray
- ML model training monitoringnot Ray
Ray
- Distributed AI model training and servingnot Comet ML
- Large-scale data processingnot Comet ML
- Reinforcement learning workloadsnot Comet ML
- ML inference servingnot Comet ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
Pricing, plan by plan
Comet ML
Free- Free CloudFree
- Up to 10 team members
- 25,000 spans per month
- 60-day data retention
- Pro Cloud$19/month
- Up to 50 team members
- 100,000 spans per month
- 60-day data retention
- MLOps FreeFree
- 1 user with fair usage policy
- Experiment tracking
- Dataset management
- MLOps Pro$19/user/month
- Up to 10 users
- 1,500 training hours included
- 500GB storage included
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
Which should you pick?
Choose Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Choose Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Questions people ask
- Is Comet ML or Ray better?
- Neither clearly leads. Comet ML starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Comet ML or Ray?
- Comet ML starts at Free and Ray at Free.
- Does Comet ML or Ray run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Ray runs on Linux, Mac, Windows.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what Ray is typically brought in for.
- What can Comet ML do that Ray cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Both handle PyTorch, TensorFlow, scikit-learn, Hugging Face.
Answered from the vendors’ own pages
Comet ML: Does Comet.ml offer a free plan?
Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.
SourceRay: Is Ray free?
Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.
SourceComet ML: How many team members can use the free Comet.ml tier?
Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.
SourceRay: Is there a paid option for Ray?
Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.
SourceComet ML: What is a span in Comet.ml pricing?
A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.
SourceRay: Can I try Ray with credits?
Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.
SourceComet ML: Does Comet.ml offer academic pricing?
Yes, a free Pro plan is available for academic users; verification is required via signup.
SourceRelated pages
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